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Authors: Houda Benhar 1 ; Ali Idri 2 ; 1 and Mohamed Hosni 3 ; 1

Affiliations: 1 Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco ; 2 Complex Systems Engineering and Human Systems, Mohammed VI Polytechnic University, Ben Guerir, Morocco ; 3 Laboratory of Mathematical Modeling, Simulation and Smart Systems, ENSAM-Meknes, Moulay ISMAIL University, Meknes, Morocco

Keyword(s): Heart Disease, Classification, Feature Selection, Ensemble Learning, Ensemble Feature Selection, Univariate Filter.

Abstract: Feature selection is a fundamental data preparation task in any data mining objective. Deciding on the best feature selection technique to use for a specific context is difficult and time-consuming. Ensemble learning can alleviate this issue. Ensemble methods are based on the assumption that the aggregate results of a group of experts with average knowledge can often be superior to those of highly knowledgeable individual ones. The present study aims to propose a heterogeneous ensemble feature selection for heart disease classification. The proposed ensembles were constructed by combining the results of five univariate filter feature selection techniques using two aggregation methods. The performance of the proposed techniques was evaluated with four classifiers and six heart disease datasets. The empirical experiments showed that applying ensemble feature ranking produced very promising results compared to single ones and previous studies.

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Paper citation in several formats:
Benhar, H.; Idri, A. and Hosni, M. (2022). Ensemble Feature Selection for Heart Disease Classification. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 369-376. DOI: 10.5220/0010800500003123

@conference{healthinf22,
author={Houda Benhar. and Ali Idri. and Mohamed Hosni.},
title={Ensemble Feature Selection for Heart Disease Classification},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF},
year={2022},
pages={369-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010800500003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF
TI - Ensemble Feature Selection for Heart Disease Classification
SN - 978-989-758-552-4
IS - 2184-4305
AU - Benhar, H.
AU - Idri, A.
AU - Hosni, M.
PY - 2022
SP - 369
EP - 376
DO - 10.5220/0010800500003123
PB - SciTePress

- Science and Technology Publications, Lda.
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